RESUMO
PURPOSE OF REVIEW: Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS: Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY: Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.